Operational Performance Parameters of Engine Inlet ...

Operational Performance Parameters of Engine Inlet Barrier Filtration Systems for Rotorcraft Nicholas Bojdo* & Antonio Filippone *Postgraduate Researcher The University of Manchester Manchester, UK [email protected]

ABSTRACT This contribution introduces addresses the subject of engine intake filtration systems for rotorcraft. The reader is firstly introduced to their purpose, which is to eliminate the risk of particle ingestion by the engine, a situation which can cause irrevocable damage to key components. The risk is significantly elevated during a condition known as brownout, in which the rotor wake of a helicopter in ground effect interacts with loose sediment, causing the generation of a dust cloud. In such a condition, IBF successfully removes particles from the engine-bound air at the expense of a pressure drop which grows temporally. This decreases the overall intake efficiency, but at varying degrees dependent on certain factors. An increase in deterioration rate is caused by but not limited to: an increase in incident velocity; and a decrease of mean particle size. However the rate may be slowed by operating the filter at a tangential angle to the flow. The present work uses CFD to carry out a parametric study into the factors affecting particle deposition and uses the results to form a semi-analytical model of pressure drop and ultimately transient intake efficiency for IBF fitted rotorcraft. NOTATION1 English AF dF dMF dT (dm/dt) D E k L m M P Q U V x

= = = = = = = = = = = = = = = =

total filter surface area filter, m2 filter medium fibre diameter, m mass collected on filter in one mass-step, kg time period of one time-step, s particle deposition rate, kgs-1 pleat depth, m porous medium thickness, m porous medium permeability, m2 filter thickness, m local mass collected, kg total mass collected, kgm static pressure, Pa volume flow rate, m-3s-1 velocity of two-phase flow, ms-1 local velocity, ms-1 depthwise distance along pleat channel, m

Greek αF ∆P ∆P* ε η

= = = = =

filter packing fraction pressure drop, Pa dimensionless pressure drop porosity efficiency

µ ρg φ

= = =

Subscripts 0 = atm = C = CR = f = F = g = m = n = p = s = t = x = engine = in = intake = local = noFilt =

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Presented at the American Helicopter Society 67th Annual Forum, Virginia Beach, VA, May 3-5, 2011. Copyright © 2011 by Nicholas Bojdo. All rights reserved.

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gas viscosity, kgm-1s-1 density, kgm-33 ratio of continuous-phase to discrete-phase volume fraction

initial state atmospheric value cake value critical value value during filtration whole filter value gas value mass-based component normal to pleat surface particle value component along pleat surface time-based x-component value at engine domain outlet value at domain inlet intake value local value no filter installed value

pressure analogous to a reduction in intake efficiency. According to manufacturers the loss is initially in the region of 600 Pa, but rises fivefold before a filter cycle is complete due to the capture of particles. The process of particle accumulation on and within the filter that gives rise to this transient state is known as clogging. With many contributory factors, filter clogging is not a simple process. The local conditions are especially influential in the evolution of the clogged filter, in particular the incident flow direction, the flow velocity magnitude, the size of the particles and concentration of the dust cloud. Hence the IBF temporal pressure drop and therefore the variation in engine performance is very much dependent on the rotorcraft operational environment. It is the objective of the current work to investigate the role of local conditions in filter clogging, and provide insight into the temporal variation in pressure drop across IBF filters in order that the effect on the engine may ultimately be predicted.

INTRODUCTION Inlet Barrier Filter systems (IBF) are an essential piece of hardware in the operation of helicopters to and from unprepared landing sites. They are installed ahead of the engine in a variety of configurations, determined by the engine-airframe integration and mass flow requirements of the engine. On single- or light twin-engine rotorcraft the filters are located within the intake plenum, or flush with the airframe; on larger rotorcraft they may resemble an external, box-like structure. An example of the latter is given in Figure 1, which depicts a Sikorsky UH-60 in operation, fitted with an IBF system at its intakes.

BACKGROUND Threat to Rotorcraft Engines The type of damage caused by the ingested particulate is wide-ranging and affects the whole engine, although it is the compressor at which performance degrades most [6]. In particular, damage to compressor blades includes blunted leading edges, sharpened trailing edges, reduced blade chords and increased pressure surface roughness. In addition to erosion, performance loss can arise from the deposition of molten impurities on combustor walls and turbine vanes, leading to flow path modification. Such observations are made from numerous experimental and numerical investigations into the various aspects of particle ingestion, erosion, and deposition; a review is given by Hamed & Tabakoff [2]. Notably, it was found that even small particles of 1 to 30 microns in size can cause severe damage to exposed components. The question of which particles pose a risk to the engine is indirectly the subject of Brownout studies. Recent investigations into the study of the Brownout phenomenon have been motivated by anecdotal evidence, which suggests that a developing dust cloud can vary in severity and extent for different rotorcraft due to certain rotor design features [7]. The difficulties encountered in modelling Brownout caused by rotor wake interaction with the ground are related to the unsteady resultant flowfield, non-uniform particulate concentrations, and the transfer of momentum and energy between the carrier and sediment phases. A short review of the current literature on the study of Brownout is included in the work of Leishman et al. (2009) [7], in which a series of experiments were performed with a two-bladed rotor system in hover over a sediment bed. Using laser-sheet imagery, the effects of rotor wake interaction in ground effect and the role of vortices in sediment uplift were studied. The work describes the mechanism of mobilisation and transport of loose particles from a sediment bed. To be released from rest, it is observed that a certain mean surface boundary flow or threshold friction velocity exists, beyond

Figure 1: Sikorsky UH-60 Blackhawk with main engine barrier filter system fitted. [1]. The necessary employment of IBF or other such particulate removal systems is best exemplified by anecdotal evidence of engine damage. Severe erosion during the Vietnam War led to engines being withdrawn after just 100 hours of service [2]. More recently during operation in the first gulf war, unprotected Ch-47 Chinook helicopters required engine overhauls after as little as 25 hours of service life [3]. The damage occurs through the ingestion of sand and dust particles, which are disturbed from the ground and lofted into the air by the rotor wake. This creates a dust cloud that engulfs the helicopter when the dust mass concentration exceeds 1.177E-03 kilograms per cubic metre of air, the condition is known as brownout [4] although the term is more generally appropriated to all situations of dust cloud generation. Based on this figure, an unprotected engine with a mass flow of 12.5 kgs-1 could ingest around 7 kg of particulate after just ten minutes in such a dust cloud. Given the statistics, it would appear a wise decision to employ some form of engine protection. There are several types of sand-filter available. For a full review see Filippone & Bojdo [5]. IBF systems represent one type of particle removal device. The system comprises one or more filter panels, through which enginebound air must pass. Particles that fail to navigate the tortuous path through the filter pores are deposited, as cleaner air continues to the engine. While IBF efficiently removing particles, employment of IBF induces a loss of

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which the aerodynamic forces acting on a particle exceed the gravitational and cohesive forces holding it down, thus causing it to move. Further release of particles was observed to occur due to bombardment by saltating particles and particles re-ingested through the rotor disk. Suspension in the adjacent flow occurs if the vertical drag on a particle is greater than its immersed weight. Hence in atmospheric winds, whenever the vertical component of the carrier fluid velocity is greater than or equal to the settling velocity of the particle (deduced from Stokes’ drag equation) suspension will occur. On impingement with the ground, convecting blade tip vortices were observed to increase the relative vertical velocity of sediment to levels well beyond the settling velocity of the particle, leading to sediment entrapment within the vortices. This process was augmented by the merging of adjacent vortices, which tended to roll-up together a blade radius downstream from the tip. The consequence of this was significant uplifting of particles into the flow domain surrounding the rotor disk. Since some of the downwash flow is ultimately re-ingested through the rotor disk, it follows that some particles may arrive at the engine intake. The size distribution of these particles is dependent on the upwash velocity; the upwash velocity is dependent on the strength and frequency of the tip vortices. It is observed that for steady one-dimensional winds, the gravitational force is the dominant force acting on most sediment particles over 50 microns diameter [8]; such particles are referred to as dust while to particles of size below this diameter, the term sand may be appropriated. Through the mechanisms described, the IBF can be expected to experience a wide range of dust and sand particles during operation.

pleat density. This suggests that pleat channel losses are more sensitive to changes in inflow conditions than filter media sources of loss. The same investigation revealed the influence of filter resistance on optimum design point. The filter resistance is determined by the internal structure of the filter medium. Generally speaking, the more tightly packed are the fibres of a filter, the greater their ability will be to capture smaller particles, but at the cost of a higher loss in pressure. In a pleated filter, a more capture-efficient medium will contribute a greater fraction of the total pressure drop, thus favouring a higher pleat density.

Figure 2: IBF Installation, illustrating ‘pleated’ filter element. [9] While such observations can be useful in IBF design, one can draw other conclusions that are more relevant to the current work. Suppose that the pleated filter design is set. The pleat density is fixed at the optimum point, and the influent volume flow rate is expected to remain relatively unchanged. Any change during operation to the pleat geometry or filter properties, will lead to an increase in pressure loss. From the earlier work it has been shown that the non-media losses are determined by the channel geometry. A narrower pleat channel results in larger degree of flow contraction hence greater shear layer losses. It was also shown that filter media losses are dependent on the filtration velocity and internal filter structure. A shallower pleat channel leads to a lower total filter surface area, while a more tightly packed medium poses greater resistance. These phenomenological effects become important during operation of an IBF, as they are all manifestations of pleated filter clogging. Hence they can be analysed to ascertain the temporal performance of IBF systems.

Unclogged Filter Performance The performance of a clean filter is dependent upon its design. When an IBF is installed on a rotorcraft, it cannot be removed during flight, which means there are times in which it will be operated in clean air. The unavoidable loss in pressure that is intrinsic to a filter’s performance must therefore be minimised. This is achieved by pleating the filter, or folding the filter material in a concertina-like formation (see Figure 2) to increase the total filter surface area. This reduces the filtration velocity or throughput velocity perpendicular to the filter surface, thus reducing the pressure drop across the filter medium. However, the act of pleating introduces a second source of pressure loss in the triangular-shaped channels that arises from flow contraction and subsequent shear layer formation. Therefore as the pleat density or number of pleats per unit span increases, for a given flow the pressure drop is seen to first decrease, before rising again due to non-media effects. This results in an optimum pleat density. The optimum design point is dependent on a number of factors, and is reflected in the chosen pleat density. The pleat density is generally set for a given intake velocity, or volume flow rate per unit intake area [9]. During prior investigation into IBF design it was found that when a larger volume flow rate is anticipated, the optimum point leans towards a lower

Clogged Filter Performance The process of filter clogging is far from simple. The theory of airflow through clean filters is well-researched and largely in agreement with experiment (see Brown, 1993 [10]). However with respect to clogging, theoretical descriptions are far less satisfactory. This is due in part to the time-dependency of the process: the fate of two particles approaching a filter will depend on which approaches first. A particle’s motion within the fluid is dependent on its size, shape, mass, temperature and velocity (linear and angular); while its adhesion to the fibres of filter or another particle will depend upon its coefficient of restitution, surface

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roughness, charge; along with all the varying fluid properties that may influence attractive and repulsive forces. However it is possible to at least deconstruct the problem to gain a greater understanding of the mechanisms at work, in order to realise the relevance of clogging in the context of IBF. First, it is useful to provide a brief description of the filter structure and the basic properties that are used to calculate pressure drop across a porous medium. Filter media for IBF consist of multiple layers of either structured woven yarn, or unstructured randomly assorted fibres (see Figure 3). The fibre material may vary, but currently cotton is used. The material fraction of the total filter volume is known as the packing fraction, which is the opposite of porosity. Generally speaking, the filter efficiency improves as packing fraction increases, but this depends upon the particle size being captured. The pressure drop is determined by the permeability which is an intrinsic constant of any porous medium, and can be considered as the filter’s ability to transmit fluid. A lower permeability means a higher pressure drop across the medium. Quantifying the permeability has been the subject of many studies, a comprehensive review of which is given in Bear (1972) [11]. In general, the Kozeny-Carman equation is often used as a basis of a permeability formulation and is combined with empirical modifications (Refs. [10], [11], [12], [13]). It is usually expressed using the symbol k, given in units of area, and is described as a function of the packing fraction and fibre diameter when applied to filters:

kF =

(1 − α F )

3

150α F

df

time reached a point of sudden and catastrophic increase. The authors consistently found a discontinuity of gradient in the plot of pressure drop against collected mass, which they termed the clogging point. At this point, the filter has reached holding capacity and particles begin to accumulate on the surface instead of within the medium, forming what is known as a filter cake. At this point, the filtration mode changes from depth filtration to surface filtration. In the second mode, the filter is essentially the cake itself, and exhibits different filtration properties to the filter medium, namely that the cake exhibits a much greater resistance.

Figure 3: Randomly assorted inorganic nanofibres. The diameter of the fibres pictured is 300 nanometres. [15]

2

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(1) The way in which the two modes evolve is largely dependent on the particle size and filtration velocity. During depth filtration, the term clogging rate can be used to describe the mass collected per unit filter volume. A higher local clogging rate leads to lower local permeability thus higher rate of pressure drop increase; local clogging rate is dependent on the mechanism of capture (see Brown [10]) of a particle by a single fibre within the filter matrix. Generally speaking, the smaller the Stokes number the more evenly distributed the distribution will be around the whole fibre. The effect of this is to improve the fibre’s ability to capture a particle, hence augmenting its collection efficiency and increasing the local resistance to flow hence an increase in pressure drop. This can be beneficial if high collection efficiency is the desired, but ultimately the filter clogs more quickly when the particle size and filtration velocity is small. As Stokes number increases and trajectories cut across streamlines, the mechanics of deposition change and the filter clogs more evenly depthwise. When the particle size exceeds the filter pore size, or when particles combine with each other to bridge filter pores, a cake forms. The resulting pressure drop is similarly a function of the cake permeability; the cake permeability is a function of the porosity and constituent particle diameter. The modelling of cake growth is somewhat eased when the cake is composed of identical particles (see Refs. [13], [16], [17]) and remains at constant porosity, but in reality this is unlikely. For example, in the current application the filter is expected to

where kF is the filter coefficient of permeability, αF is the filter packing fraction, and df is the fibre diameter. The pressure drop across a porous medium has two contributions: viscous losses; and inertial losses. The former rises linearly with the volume flow rate through the filter, while the latter increases with the square. A well-known correlation drop proposed by Ergun (1952) [14] expresses the pressure drop (in terms of Eq. 1) as: 2 µ Q 1 ρg dF  Q   ∆P =  +    EF  k F AF 86 k F α F  AF  

(2)

where ∆P is the pressure drop, µ and ρg are the gas viscosity and density respectively, Q is the volume flow rate, AF is the total filter surface area and E is the filter thickness. The volume flow rate per unit filter surface area is more commonly expressed as the superficial filtration velocity perpendicular to the filter surface Vf. It has been shown that when the fibre Reynolds number is small (Ref = ρgVFdF/µ < 10), the inertial effects are negligible [11]. This removes the right hand term from Eq. 2, leaving the more familiar Darcy equation, in which flow through a porous medium is generally assumed to be laminar. The process of clogging, in which a filter is fed continually with particulate-laden flow, exhibits two distinct phases. A number of cases are described in Brown [10] in which the pressure drop the pressure drop as a function of

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the pleat channels can be determined. Assuming the entrance channel similarity solution is still valid, this velocity field can be used to compute particle transport in the pleat channel, thus enabling a particle build-up model to be derived.

receive particle of size 0-200 microns. Furthermore a variety of particle shapes are expected. As demonstrated by Wakeman [18]: when the feed contains a size distribution, the porosity of the cake decreases due to the smaller particles occupying the pores between the large particles. When the feed contains particles of greater specific surface (or smaller shape factor) the cake resistance increases due to higher total drag. Since smaller particles also exhibit a larger specific surface, it may be wiser to base any prediction of pressure drop across the filter cake on the properties of the smallest particles. Wakeman suggests that instead of using the mean diameter (50%) of a particle size distribution as the characteristic diameter, a more reasonable estimate would be the 5 or 10 percentile size on the cumulative undersize curve. Prediction of the cake evolution is further complicated by its compressibility and fragility. Concerning the former, the drag force on a cake layer puts pressure on the adjacent layer; the summation of the two layers’ drag puts pressure on the next layer, and so on. This leads to compression of the cake, the degree of which increases with depth and results in a non-uniform porosity. Concerning the latter, the dominant adhesive force is the particle drag; other forces in filtration include van der Waals, electric forces and surface tension, which are negligible if it is assumed that all particles are of similar material, carry the same charge (if any) and that the humidity is low. The dust layer is therefore rather frail. Given the operating environment of the IBF, in which there is a prevalence of vibration, noise and occasionally large foreign objects, the difficulty of modelling cake growth can be appreciated.

Figure 4: Example drawing of a half-pleat. The two symmetric lines at top and bottom relate to the limits of the computational domain..

Clogging Scenarios for Pleated Filters One factor in the clogging of pleated filters is the velocity flowfield that develops in the pleat channels. The contraction of the fluid as it enters the pleat channel results in an uneven distribution of velocity along the pleat surface. This was demonstrated in earlier work, in which a number of simulations were performed to assess the effect of pleat shape on the local flow field and pressure drop. An example of the half-pleat is shown in Figure 4. The pleat density can also be referred to as the pleat count: since the pleat count is the number of pleats per unit filter span, it follows that the pleat width is the inverse of the pleat count. The pleat depth is the distance from the forward pleat fold edge to the rear pleat fold edge, and is denoted by D. Figure 5 shows the influence of pleat shape on local velocity magnitude at the surface of the filter. The distance along the pleat surface is given in dimensionless form, as a function of the pleat depth. A study by Rebai et al. [17] was performed to predict two clogging scenarios that vary arise from the variation in local filtration velocity described above. In earlier work by the same author it was shown that a simplified model of the two-dimensional flow through pleated filter channels, in the absence of particles, can be derived by channel crosssectional averaging of the Navier-Stokes (NS) equations and by using similarity solutions of the local NS equations [20]. From the resulting numerical solutions, the velocity field in

Figure 5: Plot of local velocity magnitude V(x) normalised with inflow velocity U, at different locations along the pleat surface. In the simulation, a porous domain of permeability coefficient 2.28 x 10-10 m2, with a pleat count of 1.00 pleats/cm (or half-pleat width of 0.5 cm), a depth of 2.5 cm, and a thickness of 0.1 cm, was subjected to an incident velocity of 5 m/s of Standard Day conditions. The model rests on some key assumptions. Firstly it is assumed that the incident flow is parallel to the axis of symmetry of the pleat (as shown also in Figure 4). Secondly it is assumed that the particles are spherical, with constant diameter of 0.99 microns (representing the median diameter of A2 Fine Arizona Test Dust). Coupled with the fluid inlet conditions, these data yield values for the Stokes, Peclet and Froude numbers as 0.2, 105 and 50 respectively. While indicating that the effects of Brownian diffusion and gravity are negligible, these numbers suggest that inertia effects cannot be neglected. However the third assumption in the investigation is that the Stokes number is effectively zero, such that the particles follow the fluid streamlines. Under this condition, the particle deposition rate is simply proportional to the local filtration velocity. The mass

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collected at a location along the pleat surface translates to a volume per unit surface area, hence a local cake thickness. The modification to the free fluid domain caused by a reduction in local channel width is then updated and the solution re-iterated. The model is used to illustrate the difference in clogging of two pleat densities. Unlike Figure 4, the pleats tested have uniform channel widths along their depths. An increase in pleat density means a narrowing of the channel width, but an increase in total filter surface area. In the first case of ‘low’ pleat density, there is found to be ‘cake lateral growth clogging’ from the channel walls. Since the local filtration velocity increases gradually from the pleat entrance, preferential growth is observed from the channel bottom. This causes the maximum filtration velocity and hence deposition rate to migrate towards the pleat entrance, resulting in a generally uniform depthwise growth over time. Conversely in the second instance when a ‘high’ pleat density (almost double that of the first case) is tested, the filtration maximum resides in the pleat bottom and remains there over time, resulting in ‘from the bottom cake growth clogging’. The two scenarios give rise varying filter performances. A given mass of particulate will settle in a different manner depending on the free fluid velocity flowfield, which itself depends on the pleat density. This results in a different rate of pressure rise for each pleat shape. The study by Rebai et al. [17] shows that for a given pressure drop, some shapes can retain more mass than others; they have a higher filtration capacity. Rather like the performance of unclogged filter, there is an optimum design point: increasing the number of pleats initially means that the captured particles are spread over a greater area; over-pleating can exacerbate the pressure drop in a way that diminishes this benefit. In the cases studied by Rebai et al. the optimum pleat density for maximum filtration capacity is found to be higher than the optimum for clean-filter pressure drop. However, the conditions maintained and assumptions instated in the work of Rebai et al. are rather ideal. In the real-world context of IBF operation, there are many differences: the particulate feed rate is unsteady; the feed is composed of a non-uniform particle size and shape distribution; and the incident flow is not necessarily parallel to the axis of pleat symmetry.

Flow Domain Two tests for comparative study were setup using the domains created. The first was setup to compare the performance of two intake configurations; the second was established to compare two differing incident flows. Three domains were designed to construct these cases, as illustrated in Figure 6. The two intake configurations are labelled free and flush, relating to their integration with the airframe. In each case a constant pleat shape was used, resembling a geometry identical to the one shown in Figure 4, but of twice the depth. The distance between two adjacent pleat folds was 0.01 m, while the fold tip-to-tip pleat depth was 0.05 m. The filter thickness was 1E-03 m and its permeability was 2.280E-10 m2. The pleat section was multiplied to span an intake of width half a metre, and thus contained 50 pleats in total. The portion of the domain representing the filter is prescribed a permeability, which extracts momentum from the flow in the form a sink term in the NS equations that amounts to the pressure drop given in Eq. 2. In the comparative study of intake configuration, the flow was initiated in the x-direction, permitting symmetry to be employed to reduce computational requirement. The domains in Figs. 6a and 6b were used to compare performances of the two intake configurations in an incident flow from left to right; Figs. 6b and 6c were used to compare the performance of a flush filter in two flows: from left to right and from top to bottom. Modelling Procedure The commercial computational fluid dynamics (CFD) package Fluent was used to solve the NS equations and compute particle trajectories. Based on the standard mass concentration of 1.177E-03 kgm-3 for brownout and a particle density of 2650 kgm-3, the dispersed phase was treated as having a low volume fraction in the continuous phase. This afforded a Euler-Lagrangian approach to modelling the two-phase system, in which the fluid phase was treated as a continuum and the dispersed phase was solved by tracking a large number of particles. In this approach, the particles can exchange momentum, mass, and energy with the fluid, however in the present work the particles were inert and the process was treated as adiabatic. Due to a lack of data upon which to base boundary conditions, the problem was considered as steady-state and was solved using the following procedure: 1. Solve the continuous-phase flow. 2. Create the discrete-phase injections. 3. Solve the coupled flow. 4. Track the discrete-phase injections. Fluent predicts the trajectory of a particle in the flow by integrating the force balance equation, which is written in a Lagrangian frame. The inertia of the particle equates to the forces exerted by the fluid upon it, which relate to the relative drag on the particle, the effect of gravity, and any other miscellaneous forces, which may include the Brownian Force, thermophoretic forces, and the Saffman Lift force. blank

DESCRIPTION OF THE COMPUTATIONAL SIMULATIONS To gain an understanding of how an IBF might clog under varying operational conditions, simulations were performed on simplified representative domains, using commercial CFD software. The software has the capability to solve the Navier-Stokes equations for the free fluid flow, while computing particle trajectories and two-way coupling between continuous and discrete phases. The goal of the present work was to examine the factors affecting particle motion prior to reaching the filter, in order to predict the temporal performance of an IBF system due to clogging.

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Due to the absence of sub-micron particles, these latter forces were neglected in the present work. The particles were treated as irrotational point-masses, whose change in momentum is extracted from the continuous-phase using a momentum sink term. At the current stage of the work, the effect of turbulent dispersion on the particles is neglected; hence the particle trajectories were based on the mean continuous flow field. Solution Setup The continuous phase properties related to air at Standard Day. The Reynolds Stress Model was used to solve for the turbulence in the continuous phase, which had a prescribed intensity of 2% and a lengthscale of 0.0007 m based on 7% of the pleat channel width. Each domain was discretised into an unstructured mesh of triangular elements; the grid sizes are 1322469 cells, 961834 cells and 2363950 cells for the domains in Figs. 6a, 6b and 6c respectively. Pressure-velocity coupling was solved with the well-known SIMPLE differencing technique, with a second-order upwind scheme. To generate results for the two studies, the inlet of each domain was prescribed with four velocities normal to the boundary: 2.5 ms-1, 5 ms-1, 10 ms-1 and 15 ms-1. In the domains in Figure 6a and 6b the boundary labelled engine is prescribed with a negative mass flow rate of 1.05 kgs-1 normal to the boundary; in Figure 6c the mass flow rate is 2.10 kgs-1. To investigate the effects of particle size distribution (PSD), cumulative mass spectra were used, that relate to two test dusts: AC Coarse made up of particles of 2200 microns diameter; and AC Fine made up of particles of 0-80 microns diameter. The cumulative mass distribution curves are shown in Figure 7, using data obtained from a commercial test dust processor [21]. The vertical axis represents the percentage by weight of the test sand that would pass through a given sieve size represented by the horizontal axis. The particles are injected along the flow inlets. In the absence of data with which to prescribe boundary conditions, it is assumed that all particles are moving with the carrier fluid with zero relative velocity i.e. posses the same velocity as the inlet flow. The two-phase volume flow rate (per unit metre in z-direction) at the inlet can be calculated from the inlet width and inlet velocity, for example 7.5 m3s-1 for an inlet velocity of 10ms-1. Combining this with the value for brownout concentration (specified as 1.177E-03 kilograms of particulate per cubic metre of twophase flow), yields a total discrete-phase inlet mass flow rate. The PSD is segregated into particle groups, for example 5-10 microns, each of which relates to the data presented in Figure 7. There is one injection group per particle group, the properties of which relate to the mean (50%) particle size of the group and respective mass fraction, for example 7.5 microns and 14%. These data can be used to determine the total mass flow rate per particle group injection. For the 5-10 micron group, the mass flow rate is thus 1.258E-06 kgm-3. Once the particle diameter and total mass flow rate are set for a group injection, Fluent requires specification of the

Figure 6: Computational fluid domains used in the present study. From top to bottom: a) “Free” domain; b) “Side” domain; c) “Flush” domain.

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software would arrest the motion of a particle at the portion of the domain representing the filter, adding its volume to the space occupied by the filter hence allowing cake growth to be accounted for. However to achieve this would require re-meshing of the grid around the filter every discrete-phase iteration, which was considered too computationally expensive at this stage. Instead a simplified model was used, in which the particle’s motion was influenced by the presence of the porous zone, but in which the particle passed straight through the porous boundaries. By tracking the particle, Fluent could record the location (in Cartesian form) at which the particle first reaches the filter, thus allowing a semi-analytical model for cake growth to be established.

number of injections, which relates to the spatial distribution of particles at the inlet. The spatial distribution of particles is difficult to measure let alone prescribe to the inlet boundary; investigation of the multitude of contributory factors is beyond the scope of the present work. Instead, an idealised spatial distribution is prescribed based on interparticle spacing. The interparticle spacing is the average distance between two adjacent particles, and is dependent on the particle group’s representative diameter and concentration. The interparticle spacing can be calculated from the following formula:

 1 1+ ϕ  L = dp  π ϕ  6

(3)

RESULTS AND DISCUSSION

Where L is the interparticle centroid distance, dp is the particle diameter and φ is the ratio of continuous-phase to discrete-phase volume fraction. Dividing the inlet area by the interparticle spacing yields the number of injections per particle group. However, preliminary runs using this method proved unsatisfactory, as “large” particle groups with a small fraction of the total mass concentration has such sparse distributions, that their occurrences on the filter amounted to just a few locations. To remedy this and create more representative and useful results, the number of injections of each particle group was multiplied by, and each particle stream mass flow reduced by, a factor of 25.

Pressure Drop Before the introduction of particles, the effect of the clean filter’s presence on static pressure and intake efficiency is presented. Due to the difference between engine inlet velocity and domain influent velocity, there is an acceleration or deceleration of the flow on entry into the intake. Generally speaking, when the influent velocity exceeds the engine inlet velocity, there is an increase in static pressure at the intake due to pressure recovery from the decelerating flow. To establish the absolute pressure drop across the filter, the simulations are run with identical domains but without the presence of a filter. The difference in static pressure between atmospheric and engine is attributed to ram effects (neglecting wall friction), and is subtracted from the static pressure results of the simulations performed with the filter. The results are shown in Figure 8, which displays the variation in filter pressure drop with influent velocity, which is given as a fraction for the engine inlet velocity. For each case there is an increase in pressure drop with velocity. This correlates with earlier work in filter pleat design. Recalling that the filter pressure drop has two sources: media-related and non-media related; the nonlinearity points toward a difference in sensitivity of the two sources to changes in velocity. However, to attribute all losses to the filter and differences to filter velocity falls short of conclusive, owing to the flow effects arising from wall interactions such as shear flow and separated flow. Bearing in mind the limitations of conclusions into the pressure drop, the data can be presented differently to achieve a greater understanding of the filter’s presence. Figure 9a shows the engine static pressure normalised with the atmospheric pressure. The increase in pressure ratio with influent velocity is symptomatic of the pressure recovery due to the deceleration of the flow before the filter. However, to realise more comprehensively the effect of the filter’s presence on static pressure it is more useful to correct the data by normalising with the results for the case when no filter is fitted. This is given in Figure 9b. The data resemble those in Figure 8 but account for non-filter losses in pressure, and illustrate a general decrease in pressure ratio (and therefore a decrease in intake efficiency) with blankblank

Figure 7: Particle Size Distribution Curves for AC Coarse and AC Fine, by cumulative mass (Source: Powder Technology Inc. [21]). Returning to the goal of the simulations: to understand the transient performance of an IBF due to clogging; the simulations were used to predict where particles of certain sizes would accumulate on a pleated filter under different conditions. The results would be analysed by comparing two different intake configurations and two unique incident flows. In the cases of the present work, a particle has two possible fates: escape through the boundaries labelled outflow or escape through the boundary engine. Ideally, the

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increasing influent velocity. Notably, there is a slight increase in pressure ratio as the velocity ratio passes through unity for the free and flush curves. This could be result of the alteration in incident flow direction at certain parts of the filter that occurs as the captured streamtube varies. Intake Capture Streamtube During analysis of the whole filter, it was found that the situation of flow parallel to the pleat axis of symmetry is rather ideal. Instead, the present work showed that the distribution of particulate is dependent on the intake’s captured streamtube. To illustrate this, it is useful to employ some graphs. Figs. 10 to 12 show the change in inlet capture streamtube for the three cases investigated, when subjected to two differing influent velocities. In Figs. 10 and 11, the effect of increasing influent velocity is to reduce the width of the captured streamtube. This occurs because of mass conservation: a higher (subsonic) incident velocity results in a smaller far field capture area for a given engine volume flow rate; the captured streamtube alters accordingly. When the freestream/engine velocity ratio is unity, the intake is said to be operating with full flow [22]. The graphs of Figure 10 and 11 suggest that the further away from the situation of full flow, the tighter is the curvature of the streamlines. This has implications for particle capture, as the motion of particles approaching the filter is dependent on their subsequent response to changes in velocity gradient. For example, fewer particles of Stokes number below unity would reach the filter of Figure 10b due to their entrainment in the fluid streamlines.

Figure 8: Plot of loss in Static Pressure across filter for different intake configuration and influent velocity. The influent velocity is non-dimensionalised with the engine average velocity (U* = Uin/Uengine; Uengine = 3.428 ms-1).

Figure 10: ‘Free’ type intake captured streamtubes for two influent velocities: a) Uin/Uengine = 0.73 (left); b) Uin/Uengine = 2.92. In comparing intake configurations, there are slight differences between the free and flush type intakes, which relate to far field capture area, with the latter capturing flow from a wider area for the same velocity ratio. When the flow approaches from a direction tangential to the filter plane surface (Figure 12), the capture area becomes significantly reduced at higher velocities.

Figure 9: Plots of dimensionless pressure drop normalised with a) ambient static pressure (top); b) nofilter fitted intake static pressure. The influent velocity is non-dimensionalised with the engine average velocity (U* = Uin/Uengine; Uengine = 3.428 ms-1).

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on the filter. In introducing particles the study of Rebai et al. is recalled, in which the clogging scenario of a given pleat was related to the local filtration velocity [17]. It was assumed that the particle trajectories followed the fluid streamlines: particle deposition rate was a function of local velocity, hence maximum cake growth occurred at regions of high velocity. However the particles used in the present work are larger and possess greater inertia, which affects their relative motion in the carrier fluid. The conclusions of Rebai et al. are therefore limited in applicability. By examining a portion of the domain in which the local conditions closely resemble those of Rebai et al., the difference can be observed.

Figure 11: ‘Flush’ type intake captured streamtubes for two influent velocities: a) U∞/Uengine = 0.73 (left); b) U∞/Uengine = 2.92.

Figure 13: Velocity and Particle Distribution Profiles along pleat surface, for injection of AC Fine Test Dust. From top to bottom: a) at 2.5 ms-1 particle injection velocity; b) at 15 ms-1 particle injection velocity. The local velocity on the right-hand y-axis is nondimensionalised with the engine average velocity (U*= Us,n/Uengine; Uengine = 3.428 ms-1).

Figure 12: ‘Side’ type intake captured streamtubes for two influent velocities: a) U∞/Uengine = 0.73 (left); b) U∞/Uengine = 2.92. Local Particle Distribution The discussion of the varying intake streamtubes becomes important when investigating particle distribution

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Displayed in Figure 13 are data from the pleat lying closest to the axis of symmetry of the domain from Figure 6a (free type intake). At this location, the effects of divergence from full flow condition on the fluid streamlines are least; hence the incident flow is normal to the filter plane. The data pertain to the mass of AC Fine Test Dust collected at different locations along the pleat surface, over a time period in which 500 grams of particulate collects over the whole filter. Hence the left-hand y-axis refers to the fraction of the total mass collected at a distance along the pleat surface. The x-axis refers to the distance along the pleat surface (the midpoint, at around 0.05 m, refers to the pleat ‘trough’ bottom) measured as a curve length. On the right-hand y-axis is given the local velocity perpendicular to the pleat surface (or filtration velocity), non-dimensionalised with the engine inlet velocity. Figure 13a shows a correlation between collected mass and local velocity distribution. This appears to support the assumption of Rebai et al., that deposition rate is proportional to filtration velocity. However, the initial incident particle velocity is relatively low, at 2.5 ms-1. At the higher injection velocity of 15 ms-1, the particle distribution becomes more even, resembling more closely the injection distribution. This suggests that the particles are being influenced less by the carrier phase, which happens due to an increase in the particles’ momentum. A comparison of the filtration velocity distributions in Figs. 13a and 13b reveals that the magnitude of the carrier phase inlet velocity has little bearing on the local conditions at the pleat surface. This is explained by considering that the flow undergoes deceleration (or acceleration) on approach to the engine to a velocity similar to that at the engine inlet plane. As the influence of the pleat channel flow conditions diminishes with particle initial velocity, it may be suggested that changing the geometry of the pleat shape has little bearing on the way in which a filter will clog, other than determining the total surface area for capture. This is in contrast to the conclusions of Rebai et al. in which filter clogging is closely related to pleat density. However, short of drawing conclusions from data of the present work, this is an area requiring more attention.

to the opposite orientation of each pleat surface: in Figure 14a the right hand line of points correspond to the pleat surfaces facing upwards (in the sense of the domain in Fig 6b), implying in this case that particle build up will be greater on these surfaces; in Figure 14b the opposite is true, in that a larger proportion of particulate collects on surfaces facing downward. Referring back to the streamlines of Figs. 10 to 12, the reasons for these differences become clear. Unlike at the local scale within the pleat channels, the velocity gradients of the fluid streamlines are gentle enough to impart enough momentum to the moving particles before reaching the filter, to influence their motion.

Overall Particle Distribution A more meaningful insight into filter clogging arises when investigating particle mass distribution over the whole filter. In this it is found that owing to the variation in streamtube shape, the influence of the carrier fluid on particle motion varies for each intake configuration, each injection velocity and each particle size distribution. Firstly, evident in all the mass distributions examined is a significant difference between adjacent pleat surfaces. Figure 14 displays plots of dimensionless mass flow rate (xaxis) versus the spatial location of each filter half-pleat (yaxis). Figure 14a shows the distribution corresponding to an inlet velocity of 2.5 ms-1; Figure 14b shows the distribution at an inlet velocity of 10 ms-1. The two distinct correlations in each figure relate to the collection of particulate on ‘upper’ and ‘lower’ pleat surface. The segregation arises due

Figure 14: Plot of particulate distribution across whole filter, of ‘flush’ intake type for two injection velocities, from top to bottom a) 2.5 ms-1 and b) 10 ms-1. Local mass flow rate is normalised with total mass flow rate of injected particulate.

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local deposition rate, which has been shown to correlate only with low local filtration velocity magnitudes.

Secondly, if the flow rates of the upper and lower surfaces of a single pleat are averaged, a more useful indication of mass distribution is attained. Figure 15 shows the mass distribution across the whole filter, when the flow approaches the filter tangential to its plane as depicted in Figure 6c. When the velocity magnitude is low the incident flow angle at the filter surface changes from being almost normal at the lower end, to being almost tangential at the upper end. This results in a generally larger rate of collection at the filter lower end, as entry streamlines exert a less severe velocity gradient on the particles. It is unclear why the scattering of flow rates towards the lower end of the filter in Fig 15a is so sporadic, although it is probably due to such a wide range of Stokes numbers and an unequal number of injections that is exhibited between particle groups. When the injection velocity is increased and inertia effects become more dominant as in Figure 15b, the result is further evidence of the explanation for a non-uniform mass distribution across the filter. Also worthy of note is the difference in mass flow rate between AC Coarse and AC fine test dusts. It is recalled that the total mass flow rate of particulate being injected into the domain is equal for each test dust. Given that the local mass flow rates plotted in Figure 15 are normalised with the total mass of particle injected, it becomes apparent that the filter captures a greater mass of particles when the dust is finer. IBF Filter Performance during Clogging: Methodology The differences in mass distribution, incident flow angle and local filtration velocity that arise from the many parameters tested in this study, lead to varying rates of filter clogging. Ultimately, this changes the performance of a given filter depending on the way it is installed into the airframe and the prevailing conditions in which it is expected to operate. To investigate how performance is affected by such factors, the data presented thus far was collated and used to establish the total filter pressure drop for each case. First, it is recalled that a planar filter clogs in two stages. The first stage is the capture of particles within the filter medium; when the filter medium reaches capacity the particles begin to collect on the filter surface as a cake, a process which is known as the second stage. The two stages are identified by a discontinuity in the plot of pressure drop versus collected mass, which arises due to the difference in permeability, or resistance to flow, of each porous medium (filter and cake). The filter permeability does not remain constant: it decreases due to the collection of particles within, which leads to the decrease in porosity, or void volume, of the porous matrix. The permeability of the cake does depend on particle size, but generally does not change with time (unless cake compression due to drag stress is considered). However unlike the filter, the cake thickness grows with the addition of more particles. As the cake grows, the geometry of the pleat channel is altered, adding another source of pressure loss that is referred to as nonmedia loss. The change in geometry is determined by the

Figure 15: Local mass distribution when filter is subjected to two tangential velocities of, from top to bottom: a) 2.5 ms-1; and b) 15 ms-1. Local mass flow rate is normalised with total mass flow rate of injected particulate. In fact, the subject of local particle deposition along the pleat surface is rather complex, owing to as yet unquantified mechanisms such as particle agglomeration, particle bounce, cake structure breakdown, and the role of moisture and charge. For this reason, the influence of local cake growth on the change in pleat channel shape and the subsequent alteration in contribution of non-media losses, are ignored

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for the time being. Instead the clogging study focuses on the contributions to pressure drop of the two media alone, and local variations in deposition rates are averaged along the half-pleat surfaces. Cake build-up is considered as a source of pressure loss, but its transient physical presence is neglected. The pressure drop across the whole filter is assumed to be an average of the different pressure drops that arise at each half-pleat. It has been shown that upper and lower pleat surfaces have rather contrasting particulate mass deposition rates. To account for this, the pressure drops of all halfpleats spanning the filter are calculated. Since the fibre and particle Reynolds numbers for the filtration velocities of each case are less than ten, inertial losses through the porous media can be ignored. Therefore recalling Eq. 2 and rewriting with symbols relating to the current problem, the pressure drop is expressed as:

 E E ( m)  F ∆P ( m ) = µ  + C  Vf  k F ( m ) kC ( d p ) 

for IBF, the value for α is borrowed from the study of Rebai et al. [17], in which the filters have application in the automotive industry. (Incidentally, the initial filter permeability is also taken from this study). The condition in Eq. 5 arises due to there being no further decrease in filter permeability when capacity is reached at critical mass. The cake permeability does not decrease with added mass, but is a function of particle diameter. Recalling the Kozeny-Carman equation from Eq. 1, the cake permeability is calculated from the following equation, in which a characteristic particle diameter of 9 microns and 12.4 microns for AC Fine and AC Coarse dusts respectively is used:

kC =

( m − M cr ) ρ p (1 − ε C )

EC = 0 for m < Mcr

(4)

∆P * ( m ) =

∆P ( m ) ∆Pm =0

(8)

Where: ∆P* is the dimensionless pressure drop across the filter; and ∆Pm=0 is the initial filter pressure drop, in clean state.

(5)

Where: Mcr is the critical mass of the filter; and ρp is the particle density taken as the density of quartz, 2650 kgm-3. The critical mass refers to the maximum mass of particulate that can be held by a filter before particles begin to collect on the filter surface. In experiments it is the value of collected mass on a planar filter medium at which the discontinuity in pressure drop occurs. For the medium used in the study of Rebai et al. from which other properties have been taken, it is given (indirectly) as 0.0297 kg. The condition in Eq. 4 arises due to the fact that the cake only begins to accumulate after critical mass is reached. The filter medium permeability decreases as collected mass increases. The rate of decrease of permeability is dependent on both the porous matrix properties and the constituent particle diameters and is given by the function:

k0 kF ( m ) = αm +1

(7)

Where: ε is the cake porosity, assumed to be 0.522 from earlier work; and dp is the characteristic mean diameter of the PSD. It is understood that using mean diameters and an assumed porosity is not exactly representative of the real situation, but they permit a low order analysis to be made at this stage, and remain under study in ongoing work by the author. The complexity of porous matrix modelling for cake is highlighted in Bear [11]. Implementing all the discussed functions into Eq. 3, the local transient pressure drop for each half-pleat can be calculated. The pressure drop across the whole filter is then derived from an average of all the half-pleat pressure drops in the filter and can be expressed in non-dimensional form by normalising with the initial pressure drop:

Where: ∆P(m) represents the local pressure drop as a function of local collected mass; µ is the viscosity; EF is the filter medium thickness; kF is the local filter permeability as a function of local collected mass; EC is the local cake thickness as a function of local collected mass; kC is the cake permeability as a function of PSD mean particle diameter; and Vf is the average local filtration velocity along the halfpleat surface, analogous to volume flow rate per unit area of filter. The cake thickness is a function of collected mass or more specifically cake volume. Assuming the cake is homogeneous, its thickness is expressed as:

EC =

3 1 εC d p 150 (1 − ε C )

IBF Filter Performance during Clogging: Results and Discussion The objective of the present work was to investigate the role of local conditions on the transient performance of an IBF. To achieve this objective with the current study, the results are presented in two forms. The first utilises the temporal change in pressure drop due to clogging. This allows a direct comparison between each case to be made to determine which parameters are the most influential in filter clogging. In this form the local collected mass m, which determines the local pressure drop, is calculated from the formula:

(

m = dm

dt

)

⋅ dT

(9)

local

Where: (dm/dt)local is the local particulate mass flow rate at the half-pleat location; and dT is the time step size. In this form, the behaviour of each IBF case when subjected to the same discrete mass concentration can be compared. The locally collected mass is fed into Eqs. 5 to 7 to yield the nondimensionless pressure drop according to Eq. 8. To best

kF(m) = kF(Mcr) for m > Mcr (6)

Where: k0 is the initial permeability of the clean filter (in this study k0 = 2.28 E-10 m-2); α is a constant that is found through experimental data; and m is the local mass collected. The evolution of rate of kF is hence determined by α. In the absence of data relating to the properties of filter media used

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The first trend to notice is that the pressure drop occurs in two stages. This is in accordance with Brown [10]: the first stage corresponds to the period of depth filtration whereby the particles are deposited within the filter medium. During this stage, the rate of efficiency deterioration is low, approximately one tenth of a percent per hour. However at around the same time in each case the pressure drop begins to grow rapidly, which translates to a sharp decline in intake efficiency. The rate of decline is different for most cases. Figure 16a compares influent velocity magnitude and intake type. When the velocity is larger the rate of decrease of efficiency is greater. This is because more particles are reaching the engine. The increase in influent velocity magnitude increases the dual-phase total mass flow approaching the engine. While the engine accounts for this by adjusting the entry streamtube of the approaching fluid, the particles possess too much momentum to be dissuaded from reaching the engine, and are captured by the filter. Figure 16a also shows that using a flush type intake can be advantageous over a free type. The rate of decrease is lower because fewer particles are reaching the filter. It is thought that this is due to the less severe velocity gradients of the fluid streamlines, which the particles do not as easily become entrained in (see Figure 12b). This is supported by the fact that the free type intake begins the period of depth filtration earlier. Figure 16b compares test dust type and incident flow angle. The data for test dust type indicate that a cake composed of finer particles leads to a faster rate of efficiency decline. This is to be expected, given the dependency of cake resistance on particle size (see Eq. 7). In comparing incident flow direction, however, an interesting result arises. The rate of efficiency decrease is significantly reduced when the flow is tangential to the filter plane. While exhibiting initially a lower efficiency, over time this configuration becomes beneficial. This may be due to an effect utilised by other intake particle separation devices, whereby a change in direction is imparted to influent flow allowing particles with too much momentum to be scavenged away. In this case, particles entering the domain posses so much inertia that they cannot negotiate the same path the continuous phase follows into the engine, thus escaping the filter altogether. This point is supported by results not shown here, in which the AC Fine dust produces a similar result but of a reduced degree due to being generally of smaller mass. The situation of a constant mass concentration during brownout is unlikely, given the countless factors involved in the generation of a dust cloud. Furthermore the duration of a brownout landing or take off will vary greatly between operations and environments, and a helicopter will descend into or out of a dust cloud during these modes. Therefore quantifying the effect of clogging on performance as a function of time is not always the best metric for comparison. Since filters are often rated by their holding capacity, a more meaningful presentation of the results shows the effect on intake efficiency as a function of collected mass over the whole filter. In this form, the locally collected mass, m, is given by:

illustrate the effect on IBF performance, the pressure drop is expressed as intake efficiency, defined as:

ηint ake =

Patm − ∆Pt =0 ⋅ ∆P *(m) Patm

(10)

Where: Patm is the atmospheric pressure; and ∆Pt=0 is the initial intake pressure loss. Expressing the pressure drop in this way includes all losses (and gains) associated with the intake, such as pressure recovery and wall friction. The results are shown in Figure 16.

Figure 16: Plots of intake efficiency with duration of time spent in brownout dust cloud, comparing: a) inlet velocity magnitude and intake type; and b) dust sample type and incident flow angle. The mass concentration is 1.177 kgm-3 and is identical for each case.

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 dm   dM  m=  ⋅ dM F  ⋅1   dt  F  dt local

(11)

Where: (dm/dt)local is the local particulate mass flow rate at the half-pleat location; (dM/dt)F is the mass deposition rate on the whole filter; and dMF is the mass increment of particulate collected on the whole filter, after which the pressure drop is re-calculated. Comparison can be further aided by removing the contribution of other sources to pressure loss, such that intake efficiency is reflective of filter performance alone. The filter-intake efficiency is given by:

ηint ake, F =

Patm − ( ∆PnoFilt + ∆Pm = 0 ⋅ ∆P *(m) ) Patm − ∆PnoFilt

(12)

Where: Patm is the atmospheric pressure; and ∆PnoFilt is the intake pressure loss when no filter is present. The results are shown in Figure 17. Of these results, significant trends are observed. First, the difference between flush and free intakes and the effect of velocity magnitude is almost eliminated. This is shown in Figure 17a where the transition to depth filtration occurs at almost the same quantity of collected mass for each case, and the efficiencies decrease at almost the same rate. The reason for this is the reduced dimensional presentation: expressing the local mass collected in terms of the total mass collected removes the dependency of the subsequent pressure drop on the quantity of particles reaching the filter. Any variation in pressure drop between cases is therefore a direct indication of the properties of the particles and the associated distribution of particles over the filter. This is supported by Figure 17b, in which the AC Fine and AC Coarse trend lines remain significantly disparate. The similarity of the two AC Coarse trend lines for normal and tangential flows suggests that the mass distribution may not be a function of flow angle. However on closer inspection the transition into depth filtration of the tangential case is much more gradual; extrapolation would reveal a gentler decrease in efficiency. This is due to the mass distribution exhibited in Figure 15b: the transition is not so sudden because different parts of the filter reach capacity at different times. Note: the fact of particles bypassing the filter through inertia is irrelevant here, as the efficiency is a response only to the mass collected on the filter.

Figure 17: Plots of filter intake efficiency with total mass collected on whole filter, comparing: a) inlet velocity magnitude and intake type; and b) dust sample type and incident flow angle.

CONCLUDING REMARKS The present work aimed to introduce the reader to the field of engine intake filtration systems for rotorcraft and provide an insight into the temporal variation in performance of barrier filters. IBF are needed to prevent engine wear during takeoff and landing in brownout conditions, allowing the lifetime of the engine to be prolonged. The construction and installation of IBF into rotorcraft is varied and specific to airframe shape, engine location and engine mass flow requirements, but the filter panels themselves are invariably pleated in formation, which creates a larger surface area for filtration and helps to reduce pressure loss through a reduction in throughput velocity. Pressure loss is an inherent

side effect of flow through a porous medium, and arises due to viscous and inertial effects within the filter pores. The process of filtration within a filter medium is complex and highly dependent on incident velocity and particle diameter, but a general rule of thumb is that a better capture ability can be achieved at the expense of a higher pressure drop. However the present work focused on the macro scale, looking instead at the mechanisms of pleatwise filter clogging to establish operational performance. Filter clogging occurs in two stages: the first in which particles collect within the filter; the second in which particles

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accumulate on the filter surface, forming a cake. The second stage forms when filter capacity has been reached (critical mass), but exhibits a greater pressure drop due to a greater flow resistance offered by the surface cake. The subject of filter clogging is made complicated by a multitude of factors that influence the arrival of particles at the filter surface. Not least important are the mechanics governing the generation of brownout condition, a subject of growing importance. Furthermore the plethora of intake types, the range of complex flows at the intake, and the huge variety of environments in which a helicopter must operate, contribute to the complicated nature of IBF performance prediction. Nevertheless the current work sets out to simplify the problem by conducting a parametric study, examining the effects of changing incident velocity magnitude, flow direction, intake configuration, and dust type, on resulting pressure drop. To ease assessment, the pressure drop was expressed in terms of intake efficiency, and explanation of exhibited trends was aided by referral to key operational features such as resultant mass distribution described earlier in the text. The implications of the results are best realised when considering the real operating environment of an IBF system. Most engine intakes are located beneath the main rotor disk and will therefore experience varying influent velocities depending on the downwash strength. Since rate of clogging increase with velocity, it follows that a helicopter with a larger wake velocity may suffer a quicker deterioration of intake efficiency. Similarly, the pressure recovery benefit of a filter panel facing into the downwash atop the helicopter may be eclipsed by the reduction in clogging of an equivalent filter mounted into the side of an airframe, the configuration of which exploits the inertia of the particles. In environments of finer dust, increase in pressure drop is likely to occur much quicker, especially when a surface cake forms, due to the constituent particles’ larger surface to volume ratio (hence drag). Quantitative analysis of the results is limited by the assumptions employed. In particular in the absence of any data from the field, filter properties such as initial permeability are assigned from a real filter used in automotive applications. Likewise, the filter dimensions are taken from a patent for an IBF, the domain geometry is simplified, and the injection properties idealised. It is hoped that with the introduction of real data, the semi-analytical approach will yield results that can be applied in a case-bycase approach to predict IBF performance. Future work also aims to incorporate a cake build-up model that includes the modification to the free fluid domain in the pleat channels

REFERENCES [1]

[2]

[3] [4]

[5]

[6]

[7]

[8]

[9] [10]

[11] [12]

[13]

[14]

[15] In summary, the present work has described qualitatively the influence of local operational conditions on IBF performance. It is part of ongoing work in which the author aims to gather all factors affecting IBF installation and subsequent operation, in order that performance may be quantified when data from the field are available.

[16]

[17]

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The U.S. Army, "A day with Black Hawk crews", flickr.com/photos/soldiersmediacenter/3843015670/, accessed: 2010-09-29 17:51:13. Hamed A., Tabakoff, W., and Wenglarz, R., “Erosion and deposition in turbomachinery”, Journal of Propulsion and Power, Vol. 22, (2), March 2006. Stallard, P., “Helicopter Engine Protection,” Perfusion, vol. 1997, (12), December 1997. Taslim M. E., and Spring, S., “A Numerical Study of Sand Particle Distribution, Density, and Shape Effects on the Scavenge Efficiency of Engine Inlet Particle Separator Systems”, Journal of the American Helicopter Society, Vol. 55, (2), April 2010. Filippone A., and Bojdo, N., “Turboshaft engine air particle separation,” Progress in Aerospace Sciences, Vol. 46, (5), July 2010. Van Der Walt J. P., and Nurick, A., “Erosion of DustFiltered Helicopter Turbine Engines Part I: Basic Theoretical Considerations,” Journal of Aircraft, Vol. 32, (1), January 1995. Leishman, J., “Investigation of Sediment Entrainment in Brownout Using High-Speed Particle Image Velocimetry”, 65th Annual American Helicopter Society Forum, Grapevine, Texas, May 2009. Greeley R. and Iversen J. D., Wind as a Geological Process: On Earth, Mars, Venus and Titan, Cambridge University Press, 1987. Scimone, M. J., “Aircraft Engine Air Filter and Method,” U.S. Patent 6595742, July 2003. Brown R.C., Air Filtration: An Integrated Approach to the Theory and Applications of Fibrous Filters, Pergamon Press, 1993. Bear J., Dynamics of Fluids in Porous Media, 1st Ed., Elsevier, 1972. Bénesse, M., Le Coq, L., and Solliec, C., “Collection efficiency of a woven filter made of multifiber yarn: Experimental characterization during loading and clean filter modeling based on a two-tier single fiber approach,” Journal of Aerosol Science, Vol. 37, (8), August 2006. Aguiar M., and Coury, J., “Cake Formation in Fabric Filtration of Gases,” Industrial and Engineering Chemistry Research, Vol. 35, (10), October 1996. Ergun S., and Orning, A. A., “Fluid Flow through Randomly Packed Columns and Fluidized Beds,” Industrial & Engineering Chemistry, Vol. 41, (6), June 1949. BASF - The Chemical Company, "A whiff of nothing", flickr.com/photos/basf/4837712716/, accessed: 2010-10-06 12:00:36. Chen Y., and Hsiau, S., “Cake formation and growth in cake filtration,” Powder Technology, Vol. 192, (2), June 2009. Rebaï, M, Prat, M., Meireles, M., Schmitz, P., and Baclet, R., “Clogging modeling in pleated filters for gas filtration,” Chemical Engineering Research and

Design, Vol. 88, (4), April 2010. [18] Wakeman, R., “The influence of particle properties on filtration,” Separation and Purification Technology, Vol. 58, (2), December 2007. [19] Rebaï, M., Prat, M., Meireles, M., Schmitz, P., and Baclet, R., “A semi-analytical model for gas flow in

pleated filters,” Chemical Engineering Science, Vol. 65, no. 9, May 2010. [20] Particle Technology, “AC Coarse and AC Fine Test Dust Data,” September 2009. [21] E. L. Goldsmith and J. Seddon, Practical Intake Aerodynamic Design. Blackwell Scientific, 1993.

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